In this file we are going to perform an exploratory data analysis for AlMetric data. Please have in mind the methodology in use, i.e. CRISP-DM have some stages such as data retention, data cleaning and etc. which is necessary for EDA, except data retention which is done via AlMetric platform, other necessary steps will be done in this notebook.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn import preprocessing
pd.options.display.max_columns = 200
sns.set_theme(style='darkgrid')
In this section we are going to import the gathered data in the code for further analysis.
atu = pd.read_csv('data/allameh.csv')
atu['University'] = "Allameh Tabataba'i University"
aut = pd.read_csv('data/amirkabir.csv')
aut['University'] = "Amir Kabir University"
sbu = pd.read_csv('data/beheshti.csv')
sbu['University'] = "Shahid Beheshti University"
fum = pd.read_csv('data/ferdowsi.csv')
fum['University'] = "Ferdowsi University of Mashhad"
ugui = pd.read_csv('data/guilan.csv')
ugui['University'] = "University of Guilan"
ihu = pd.read_csv('data/imamhosein.csv')
ihu['University'] = "Imam Hossein University"
uisf = pd.read_csv('data/isfahan.csv')
uisf['University'] = "University of Isfahan"
iut = pd.read_csv('data/iut.csv')
iut['University'] = "Isfahan University of Technology"
knu = pd.read_csv('data/knu.csv')
knu['University'] = "K. N. Toosi University of Technology"
sut = pd.read_csv('data/sharif.csv')
sut['University'] = "Sharif University of Technology"
ushi = pd.read_csv('data/shiraz.csv')
ushi['University'] = "University of Shiraz"
iust = pd.read_csv('data/stu.csv')
iust['University'] = "Iran University of Science and Technology"
utab = pd.read_csv('data/tabriz.csv')
utab['University'] = "University of Tabriz"
tmu = pd.read_csv('data/tarbiatmodares.csv')
tmu['University'] = "Tarbiat Modares University"
uteh = pd.read_csv('data/ut.csv')
uteh['University'] = "University of Tehran"
In the next section we are going to transform imported data to our needs and some feature generations are in order, i.e. label encoding two categorical variables, renaming features for standard pandas practice and etc. Another important thing we are doing in the cell below, is that we will split the column "Subjects_FoR" to main categories, to the length of the record with the highest category items.
On the context of categories, it's worth to mention some explanation regarding the matter. Based on the retrieved data, the Feature "Subjects_FoR" contains all categories for each article, these categories are based on standard system of categorization of AltMetric, the categories which starts with a 2 digit number, are major categories and those one which starts with 4 digit numbers are the sub categories of their corresponding major cateogry. For this scientific endeavor we will only focus on the major categories.
As we explored this feature, we found out that the article with the most major categories actually have 7 major categories, thus we create 7 columns one for each major categories, obviously the categories with greater number are more probable to have "No Category" as value.
df = pd.concat([atu, aut, sbu, fum, ugui, ihu, uisf, iut,
knu, sut, ushi, iust, utab, tmu, uteh])
df.drop(['Authors at my Institution', 'Departments', 'Journal ISSNs', 'Sustainable Development Goals', 'ISBN', 'National Clinical Trial ID', 'URI', 'PubMed ID', 'PubMedCentral ID', 'Handle.net IDs', 'ADS Bibcode', 'arXiv ID', 'RePEc ID', 'SSRN', 'URN', 'Details Page URL', 'Badge URL', 'Syllabi mentions', 'DOI', 'Funder'], axis=1, inplace=True)
df.columns = df.columns.str.replace(' ', '_')
df = df.reset_index(drop=True)
df['Subjects_(FoR)'].fillna('00 No Category', inplace=True)
df['Journal/Collection_Title'].fillna('No Title', inplace=True)
main_lst = []
for record in df['Subjects_(FoR)'].str.split('; '):
temp_lst = []
for element in record:
if len(element.split(' ')[0]) == 2:
temp_lst.append(element)
main_lst.append(temp_lst)
temp_df = pd.DataFrame(main_lst, columns=[
'Category_1',
'Category_2',
'Category_3',
'Category_4',
'Category_5',
'Category_6',
'Category_7',
])
temp_df.fillna('00 No Category', inplace=True)
df = pd.concat([df, temp_df], axis=1)
label_encoder = preprocessing.LabelEncoder()
df['Journal/Collection_Title_LE'] = label_encoder.fit_transform(df['Journal/Collection_Title'])
df['Output_Type_LE'] = label_encoder.fit_transform(df['Output_Type'])
df['OA_Status_LE'] = label_encoder.fit_transform(df['OA_Status'])
df['OA_Type_LE'] = label_encoder.fit_transform(df['OA_Type'])
df['Publisher_Names_LE'] = label_encoder.fit_transform(df['Publisher_Names'])
df['University_LE'] = label_encoder.fit_transform(df['University'])
df['Category_1_LE'] = label_encoder.fit_transform(df['Category_1'])
df['Category_2_LE'] = label_encoder.fit_transform(df['Category_2'])
df['Category_3_LE'] = label_encoder.fit_transform(df['Category_3'])
df['Category_4_LE'] = label_encoder.fit_transform(df['Category_4'])
df['Category_5_LE'] = label_encoder.fit_transform(df['Category_5'])
df['Category_6_LE'] = label_encoder.fit_transform(df['Category_6'])
df['Category_7_LE'] = label_encoder.fit_transform(df['Category_7'])
df['Publication_Date'] = pd.to_datetime(df['Publication_Date'])
df['OA_Type'].value_counts()
closed 48155 gold 11523 green 9690 bronze 2656 hybrid 1985 Name: OA_Type, dtype: int64
In the table below you can see the top 5 and bottom 5 records in the main dataframe. In this dataframe we have done the feature engineering generation phase.
This dataframe consists of some features that we will going to descrive each one of them:
Features with LE at their end is label encoded version of a corresponding feature.
df
| Altmetric_Attention_Score | Title | Journal/Collection_Title | Output_Type | OA_Status | OA_Type | Subjects_(FoR) | Affiliations_(GRID) | Publication_Date | News_mentions | Blog_mentions | Policy_mentions | Patent_mentions | Twitter_mentions | Peer_review_mentions | Weibo_mentions | Facebook_mentions | Wikipedia_mentions | Google+_mentions | LinkedIn_mentions | Reddit_mentions | Pinterest_mentions | F1000_mentions | Q&A_mentions | Video_mentions | Number_of_Mendeley_readers | Number_of_Dimensions_citations | Publisher_Names | University | Category_1 | Category_2 | Category_3 | Category_4 | Category_5 | Category_6 | Category_7 | Journal/Collection_Title_LE | Output_Type_LE | OA_Status_LE | OA_Type_LE | Publisher_Names_LE | University_LE | Category_1_LE | Category_2_LE | Category_3_LE | Category_4_LE | Category_5_LE | Category_6_LE | Category_7_LE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1629 | COVID-19 and male reproductive function: a pro... | Reproduction | Article | True | bronze | 11 Medical and Health Sciences; 1114 Paediatri... | Allameh Tabataba'i University; University of G... | 2021-03-01 | 87 | 1 | 0 | 0 | 2537 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 112 | 50 | NaN | Allameh Tabataba'i University | 11 Medical and Health Sciences | 31 Biological Sciences | 32 Biomedical and Clinical Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 7146 | 0 | 1 | 0 | 189 | 0 | 11 | 23 | 22 | 0 | 0 | 0 | 0 |
| 1 | 906 | The effects of three different exercise modali... | Reproduction | Article | True | bronze | 11 Medical and Health Sciences; 1103 Clinical ... | Academic Center for Education, Culture and Res... | 2017-02-01 | 128 | 1 | 0 | 0 | 22 | 0 | 0 | 6 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 117 | 38 | NaN | Allameh Tabataba'i University | 11 Medical and Health Sciences | 31 Biological Sciences | 32 Biomedical and Clinical Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 7146 | 0 | 1 | 0 | 189 | 0 | 11 | 23 | 22 | 0 | 0 | 0 | 0 |
| 2 | 239 | Fear, Loss, Social Isolation, and Incomplete G... | Basic And Clinical Neuroscience | Article | True | gold | 11 Medical and Health Sciences; 1117 Public He... | Allameh Tabataba'i University; Charles R. Drew... | 2020-07-30 | 30 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 190 | 52 | NaN | Allameh Tabataba'i University | 11 Medical and Health Sciences | 42 Health Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 1048 | 0 | 1 | 2 | 189 | 0 | 11 | 34 | 0 | 0 | 0 | 0 | 0 |
| 3 | 175 | Foundations of Social Policy and Welfare in Islam | No Title | Chapter | False | closed | 16 Studies in Human Society; 1605 Policy and A... | Allameh Tabataba'i University | 2020-12-19 | 21 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | Springer Nature | Allameh Tabataba'i University | 16 Studies in Human Society | 22 Philosophy and Religious Studies | 50 Philosophy and Religious Studies | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 6346 | 2 | 0 | 1 | 171 | 0 | 16 | 21 | 39 | 0 | 0 | 0 | 0 |
| 4 | 102 | Laughter yoga versus group exercise program in... | International Journal of Geriatric Psychiatry | Article | True | green | 11 Medical and Health Sciences; 1103 Clinical ... | Allameh Tabataba'i University; Imam Khomeini H... | 2010-09-16 | 9 | 3 | 2 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 317 | 131 | ESSOAr; Natural History Museum; Wiley | Allameh Tabataba'i University | 11 Medical and Health Sciences | 32 Biomedical and Clinical Sciences | 42 Health Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 3846 | 0 | 1 | 3 | 75 | 0 | 11 | 24 | 32 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 74004 | 0 | AN AHP MODEL FOR CROP PLANNING WITHIN IRRIGATI... | Irrigation & Drainage | Article | False | closed | 09 Engineering; 0905 Civil Engineering; 30 Agr... | University of Tehran | 2011-09-29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 7 | ESSOAr; Natural History Museum; Wiley | University of Tehran | 09 Engineering | 30 Agricultural, Veterinary and Food Sciences | 40 Engineering | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 4211 | 0 | 0 | 1 | 75 | 14 | 9 | 22 | 30 | 0 | 0 | 0 | 0 |
| 74005 | 0 | GC–MS Determination of PAHs in Fish Samples Fo... | Chromatographia | Article | False | closed | 03 Chemical Sciences; 0301 Analytical Chemistr... | University of Tehran | 2011-07-14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 14 | Springer Nature | University of Tehran | 03 Chemical Sciences | 34 Chemical Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 1620 | 0 | 0 | 1 | 171 | 14 | 3 | 26 | 0 | 0 | 0 | 0 | 0 |
| 74006 | 0 | Interface thermal resistance and thermal recti... | Applied Physics Letters | Article | False | closed | 02 Physical Sciences; 09 Engineering; 10 Techn... | University of Tehran | 2011-08-01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 81 | 103 | American Institute of Physics (AIP) | University of Tehran | 02 Physical Sciences | 09 Engineering | 10 Technology | 51 Physical Sciences | 00 No Category | 00 No Category | 00 No Category | 732 | 0 | 0 | 1 | 12 | 14 | 2 | 8 | 7 | 24 | 0 | 0 | 0 |
| 74007 | 0 | The systematic importance of anatomical data i... | Botanical Journal of the Linnean Society | Article | True | bronze | 06 Biological Sciences; 0603 Evolutionary Biol... | Queen Mary University of London; Royal Botanic... | 2010-10-11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 7 | Oxford University Press (OUP); Wiley | University of Tehran | 06 Biological Sciences | 31 Biological Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 1267 | 0 | 1 | 0 | 150 | 14 | 6 | 23 | 0 | 0 | 0 | 0 | 0 |
| 74008 | 0 | Pollen morphology of the genus Gagea (Liliacea... | Flora | Article | False | closed | 06 Biological Sciences; 0607 Plant Biology; 31... | University of Tehran | 2005-04-01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 20 | Elsevier BV | University of Tehran | 06 Biological Sciences | 31 Biological Sciences | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 00 No Category | 2723 | 0 | 0 | 1 | 78 | 14 | 6 | 23 | 0 | 0 | 0 | 0 | 0 |
74009 rows × 49 columns
First thing first, we must compare the number of article for each university. In the plot below, you can see the number of articles per university, sorted in a descending order.
As you can see the top university on the number of research output is University of Tehran with more than 16,000 articles. In the second and third place are Tarbiat Modares University and Sharif University of Technology. The lowest three universities on the context of research output are: Imam Hossein University, Allameh Tabataba'i University and University of Guilan.
x = list(df['University'].value_counts().index)
y = list(df['University'].value_counts())
plt.figure(figsize=(7, 4))
ax = sns.barplot(data=df, x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for each University')
plt.xticks(rotation=90)
plt.show()
In the plot below, you can see the top 30 journal or collection in which universities have published their research output.
The first place is No Title, this fact is due to the some of research outputs are not articles published in journals, they are books, chapters and news, this kind of articles have no corresponding journal title and this feature is due to this fact.
The journal with the most hosted articles are Scientific Reports which is a open access scientific journal under Nature and was the 5th most cited journal in the world.
On the second and third place we have Journal of High Energy Physics and PLOS ONE.
x = list(df['Journal/Collection_Title'].value_counts().head(30).index)
y = list(df['Journal/Collection_Title'].value_counts().head(30))
plt.figure(figsize=(15, 5))
ax = sns.barplot(x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for top 30 Collection / Journal')
plt.xticks(rotation=90)
plt.show()
In the plot below, you can see the top 30 Publisher in which universities have published their research output.
The publisher with the most articles of these universities are Elsevier BV with more than 21,000 and in the second place with more than 13,000 articles is Springer Nature.
x = list(df['Publisher_Names'].value_counts().head(30).index)
y = list(df['Publisher_Names'].value_counts().head(30))
plt.figure(figsize=(15, 5))
ax = sns.barplot(x=x, y=y)
ax.bar_label(ax.containers[0])
plt.title('Number of Articles for top 30 Publisher')
plt.xticks(rotation=90)
plt.show()
In the following section we will explore the categories quantity status. The plot in the following cell, is the barplot of the 7 category columns.
fig, axes = plt.subplots(7, 1, figsize=(20, 50))
x = [
list(df['Category_1'].value_counts().index),
list(df['Category_2'].value_counts().index),
list(df['Category_3'].value_counts().index),
list(df['Category_4'].value_counts().index),
list(df['Category_5'].value_counts().index),
list(df['Category_6'].value_counts().index),
list(df['Category_7'].value_counts().index),
]
y = [
list(df['Category_1'].value_counts()),
list(df['Category_2'].value_counts()),
list(df['Category_3'].value_counts()),
list(df['Category_4'].value_counts()),
list(df['Category_5'].value_counts()),
list(df['Category_6'].value_counts()),
list(df['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0], x=x[0], y=y[0])
axes[0].set_title('Number of First Categories used in Articles')
axes[0].bar_label(axes[0].containers[0])
axes[0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1], x=x[1], y=y[1])
axes[1].set_title('Number of Second Categories used in Articles')
axes[1].bar_label(axes[1].containers[0])
axes[1].tick_params(labelrotation=90)
sns.barplot(ax=axes[2], x=x[2], y=y[2])
axes[2].set_title('Number of Third Categories used in Articles')
axes[2].bar_label(axes[2].containers[0])
axes[2].tick_params(labelrotation=90)
sns.barplot(ax=axes[3], x=x[3], y=y[3])
axes[3].set_title('Number of Fourth Categories used in Articles')
axes[3].bar_label(axes[3].containers[0])
axes[3].tick_params(labelrotation=90)
sns.barplot(ax=axes[4], x=x[4], y=y[4])
axes[4].set_title('Number of Fifth Categories used in Articles')
axes[4].bar_label(axes[4].containers[0])
axes[4].tick_params(labelrotation=90)
sns.barplot(ax=axes[5], x=x[5], y=y[5])
axes[5].set_title('Number of Sixth Categories used in Articles')
axes[5].bar_label(axes[5].containers[0])
axes[5].tick_params(labelrotation=90)
sns.barplot(ax=axes[6], x=x[6], y=y[6])
axes[6].set_title('Number of Seventh Categories used in Articles')
axes[6].bar_label(axes[6].containers[0])
axes[6].tick_params(labelrotation=90)
fig.tight_layout()
plt.show()
In the Cells Below we will explore the 7 Categories for each University.
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_1'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_2'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_3'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_4'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_5'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_6'].value_counts().index),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_1'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_2'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_3'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_4'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_5'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_6'].value_counts()),
list(df[df['University'] == "Allameh Tabataba'i University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Allameh Tabataba'i University")
axes[0, 0].bar_label(axes[0, 0].containers[0])
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Allameh Tabataba'i University")
axes[0, 1].bar_label(axes[0, 1].containers[0])
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Allameh Tabataba'i University")
axes[0, 2].bar_label(axes[0, 2].containers[0])
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Allameh Tabataba'i University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Allameh Tabataba'i University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Amir Kabir University"]['Category_1'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_2'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_3'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_4'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_5'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_6'].value_counts().index),
list(df[df['University'] == "Amir Kabir University"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Amir Kabir University"]['Category_1'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_2'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_3'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_4'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_5'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_6'].value_counts()),
list(df[df['University'] == "Amir Kabir University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Amir Kabir University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Amir Kabir University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Amir Kabir University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Amir Kabir University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Amir Kabir University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Amir Kabir University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Amir Kabir University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Shahid Beheshti University"]['Category_1'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_2'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_3'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_4'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_5'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_6'].value_counts().index),
list(df[df['University'] == "Shahid Beheshti University"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Shahid Beheshti University"]['Category_1'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_2'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_3'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_4'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_5'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_6'].value_counts()),
list(df[df['University'] == "Shahid Beheshti University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Shahid Beheshti University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Shahid Beheshti University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Shahid Beheshti University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Shahid Beheshti University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Shahid Beheshti University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Shahid Beheshti University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Shahid Beheshti University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_1'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_2'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_3'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_4'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_5'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_6'].value_counts().index),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_1'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_2'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_3'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_4'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_5'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_6'].value_counts()),
list(df[df['University'] == "Ferdowsi University of Mashhad"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Ferdowsi University of Mashhad")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Ferdowsi University of Mashhad")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Ferdowsi University of Mashhad")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "University of Guilan"]['Category_1'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_2'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_3'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_4'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_5'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_6'].value_counts().index),
list(df[df['University'] == "University of Guilan"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "University of Guilan"]['Category_1'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_2'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_3'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_4'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_5'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_6'].value_counts()),
list(df[df['University'] == "University of Guilan"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Guilan")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Guilan")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Guilan")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Guilan")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Guilan")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Guilan")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Guilan")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Imam Hossein University"]['Category_1'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_2'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_3'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_4'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_5'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_6'].value_counts().index),
list(df[df['University'] == "Imam Hossein University"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Imam Hossein University"]['Category_1'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_2'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_3'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_4'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_5'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_6'].value_counts()),
list(df[df['University'] == "Imam Hossein University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Imam Hossein University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Imam Hossein University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Imam Hossein University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Imam Hossein University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Imam Hossein University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Imam Hossein University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Imam Hossein University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "University of Isfahan"]['Category_1'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_2'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_3'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_4'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_5'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_6'].value_counts().index),
list(df[df['University'] == "University of Isfahan"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "University of Isfahan"]['Category_1'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_2'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_3'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_4'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_5'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_6'].value_counts()),
list(df[df['University'] == "University of Isfahan"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Isfahan")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Isfahan")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Isfahan")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Isfahan")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Isfahan")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Isfahan")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Isfahan")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Isfahan University of Technology"]['Category_1'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_2'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_3'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_4'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_5'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_6'].value_counts().index),
list(df[df['University'] == "Isfahan University of Technology"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Isfahan University of Technology"]['Category_1'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_2'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_3'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_4'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_5'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_6'].value_counts()),
list(df[df['University'] == "Isfahan University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Isfahan University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Isfahan University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Isfahan University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Isfahan University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Isfahan University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Isfahan University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Isfahan University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_1'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_2'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_3'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_4'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_5'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_6'].value_counts().index),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_1'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_2'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_3'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_4'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_5'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_6'].value_counts()),
list(df[df['University'] == "K. N. Toosi University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of K. N. Toosi University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of K. N. Toosi University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of K. N. Toosi University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Sharif University of Technology"]['Category_1'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_2'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_3'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_4'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_5'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_6'].value_counts().index),
list(df[df['University'] == "Sharif University of Technology"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Sharif University of Technology"]['Category_1'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_2'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_3'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_4'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_5'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_6'].value_counts()),
list(df[df['University'] == "Sharif University of Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Sharif University of Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Sharif University of Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Sharif University of Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Sharif University of Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Sharif University of Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Sharif University of Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Sharif University of Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "University of Shiraz"]['Category_1'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_2'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_3'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_4'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_5'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_6'].value_counts().index),
list(df[df['University'] == "University of Shiraz"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "University of Shiraz"]['Category_1'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_2'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_3'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_4'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_5'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_6'].value_counts()),
list(df[df['University'] == "University of Shiraz"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Shiraz")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Shiraz")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Shiraz")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Shiraz")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Shiraz")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Shiraz")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Shiraz")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Iran University of Science and Technology"]['Category_1'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_2'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_3'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_4'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_5'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_6'].value_counts().index),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Iran University of Science and Technology"]['Category_1'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_2'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_3'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_4'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_5'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_6'].value_counts()),
list(df[df['University'] == "Iran University of Science and Technology"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Science and Technology")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Science and Technology")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Science and Technology")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Science and Technology")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Science and Technology")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Science and Technology")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Science and Technology")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "University of Tabriz"]['Category_1'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_2'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_3'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_4'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_5'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_6'].value_counts().index),
list(df[df['University'] == "University of Tabriz"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "University of Tabriz"]['Category_1'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_2'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_3'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_4'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_5'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_6'].value_counts()),
list(df[df['University'] == "University of Tabriz"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Tabriz")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Tabriz")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Tabriz")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Tabriz")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Tabriz")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Tabriz")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Tabriz")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "Tarbiat Modares University"]['Category_1'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_2'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_3'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_4'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_5'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_6'].value_counts().index),
list(df[df['University'] == "Tarbiat Modares University"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "Tarbiat Modares University"]['Category_1'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_2'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_3'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_4'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_5'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_6'].value_counts()),
list(df[df['University'] == "Tarbiat Modares University"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of Tarbiat Modares University")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of Tarbiat Modares University")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of Tarbiat Modares University")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of Tarbiat Modares University")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of Tarbiat Modares University")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of Tarbiat Modares University")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of Tarbiat Modares University")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(3, 3, figsize=(20, 20))
x = [
list(df[df['University'] == "University of Tehran"]['Category_1'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_2'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_3'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_4'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_5'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_6'].value_counts().index),
list(df[df['University'] == "University of Tehran"]['Category_7'].value_counts().index),
]
y = [
list(df[df['University'] == "University of Tehran"]['Category_1'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_2'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_3'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_4'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_5'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_6'].value_counts()),
list(df[df['University'] == "University of Tehran"]['Category_7'].value_counts()),
]
sns.barplot(ax=axes[0, 0], x=x[0], y=y[0])
axes[0, 0].set_title("Number of First Categories used in Articles of University of Tehran")
axes[0, 0].bar_label(axes[0, 0].containers[0], rotation=90)
axes[0, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 1], x=x[1], y=y[1])
axes[0, 1].set_title("Number of Second Categories used in Articles of University of Tehran")
axes[0, 1].bar_label(axes[0, 1].containers[0], rotation=90)
axes[0, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[0, 2], x=x[2], y=y[2])
axes[0, 2].set_title("Number of Third Categories used in Articles of University of Tehran")
axes[0, 2].bar_label(axes[0, 2].containers[0], rotation=90)
axes[0, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 0], x=x[3], y=y[3])
axes[1, 0].set_title("Number of Fourth Categories used in Articles of University of Tehran")
axes[1, 0].bar_label(axes[1, 0].containers[0])
axes[1, 0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 1], x=x[4], y=y[4])
axes[1, 1].set_title("Number of Fifth Categories used in Articles of University of Tehran")
axes[1, 1].bar_label(axes[1, 1].containers[0])
axes[1, 1].tick_params(labelrotation=90)
sns.barplot(ax=axes[1, 2], x=x[5], y=y[5])
axes[1, 2].set_title("Number of Sixth Categories used in Articles of University of Tehran")
axes[1, 2].bar_label(axes[1, 2].containers[0])
axes[1, 2].tick_params(labelrotation=90)
sns.barplot(ax=axes[2, 1], x=x[6], y=y[6])
axes[2, 1].set_title("Number of Seventh Categories used in Articles of University of Tehran")
axes[2, 1].bar_label(axes[2, 1].containers[0])
axes[2, 1].tick_params(labelrotation=90)
fig.delaxes(axes[2, 0])
fig.delaxes(axes[2, 2])
fig.tight_layout()
plt.show()
fig, axes = plt.subplots(1, 3, figsize=(10, 5))
x = [
list(df['Output_Type'].value_counts().index),
list(df['OA_Status'].value_counts().index),
list(df['OA_Type'].value_counts().index),
]
y = [
list(df['Output_Type'].value_counts()),
list(df['OA_Status'].value_counts()),
list(df['OA_Type'].value_counts()),
]
sns.barplot(ax=axes[0], x=x[0], y=y[0])
axes[0].set_title('Research Output Type')
axes[0].bar_label(axes[0].containers[0])
axes[0].tick_params(labelrotation=90)
sns.barplot(ax=axes[1], x=x[1], y=y[1])
axes[1].set_title('Open Access Status')
axes[1].bar_label(axes[1].containers[0])
axes[1].tick_params(labelrotation=90)
sns.barplot(ax=axes[2], x=x[2], y=y[2])
axes[2].set_title('Open Access Type')
axes[2].bar_label(axes[2].containers[0])
axes[2].tick_params(labelrotation=90)
plt.show()
In the plot below you can see the trend of publication based on the date. It's obvious that the trend is growing and more research output is generated. This trait might be due to the increase of tracking research outputs of investigated universities.
plt.figure(figsize=(15, 5))
ax = sns.histplot(data=df.loc[df['Publication_Date'] >= '2000'], x='Publication_Date', kde=True)
ax.bar_label(ax.containers[0])
plt.title('The Number of published articles after 2000')
plt.show()
In the next cell you can the statistical description of each features.
df.describe()
| Altmetric_Attention_Score | News_mentions | Blog_mentions | Policy_mentions | Patent_mentions | Twitter_mentions | Peer_review_mentions | Weibo_mentions | Facebook_mentions | Wikipedia_mentions | Google+_mentions | LinkedIn_mentions | Reddit_mentions | Pinterest_mentions | F1000_mentions | Q&A_mentions | Video_mentions | Number_of_Mendeley_readers | Number_of_Dimensions_citations | Journal/Collection_Title_LE | Output_Type_LE | OA_Status_LE | OA_Type_LE | Publisher_Names_LE | University_LE | Category_1_LE | Category_2_LE | Category_3_LE | Category_4_LE | Category_5_LE | Category_6_LE | Category_7_LE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 | 74009.000000 |
| mean | 3.067195 | 0.138929 | 0.039576 | 0.018228 | 0.161872 | 2.469929 | 0.020822 | 0.000189 | 0.081828 | 0.071640 | 0.009904 | 0.000014 | 0.007526 | 0.000041 | 0.000919 | 0.001040 | 0.008594 | 34.362929 | 21.984380 | 4554.856396 | 0.153941 | 0.349322 | 1.462133 | 114.151536 | 8.645057 | 7.200935 | 26.060047 | 9.685403 | 1.418706 | 0.203137 | 0.029051 | 0.000041 |
| std | 33.615673 | 2.703754 | 0.400570 | 0.190594 | 1.664764 | 46.029952 | 0.289693 | 0.022659 | 0.766598 | 1.365636 | 0.243656 | 0.003676 | 0.121293 | 0.006367 | 0.035627 | 0.033874 | 0.168230 | 68.950118 | 59.464531 | 2240.940423 | 0.531887 | 0.476759 | 0.862054 | 50.320941 | 4.359108 | 4.228762 | 9.891273 | 14.353519 | 4.742279 | 1.642100 | 0.519035 | 0.006367 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 2.000000 | 2606.000000 | 0.000000 | 0.000000 | 1.000000 | 78.000000 | 5.000000 | 3.000000 | 23.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 16.000000 | 9.000000 | 4876.000000 | 0.000000 | 0.000000 | 1.000000 | 98.000000 | 9.000000 | 8.000000 | 26.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 37.000000 | 24.000000 | 6346.000000 | 0.000000 | 1.000000 | 2.000000 | 171.000000 | 13.000000 | 9.000000 | 32.000000 | 22.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 4568.000000 | 290.000000 | 34.000000 | 12.000000 | 248.000000 | 8949.000000 | 19.000000 | 4.000000 | 109.000000 | 227.000000 | 34.000000 | 1.000000 | 11.000000 | 1.000000 | 4.000000 | 2.000000 | 17.000000 | 2915.000000 | 8264.000000 | 8192.000000 | 3.000000 | 1.000000 | 4.000000 | 189.000000 | 14.000000 | 42.000000 | 43.000000 | 41.000000 | 25.000000 | 20.000000 | 13.000000 | 1.000000 |
In the next cell we are going to visualize a scatter plot for each feature pair. Since we have a good amount of features in the dataset, this plot will be huge. To mitigate this problem, we will remove features with less than 0.25 standard deviation. The Standard Deviation Values are presented in the table above.
target_features = list(df.describe().columns[:-13])
temp = []
for feature in target_features:
if df.describe()[feature]['std'] >= 0.25:
temp.append(feature)
target_features = temp
axes = pd.plotting.scatter_matrix(df[target_features], figsize=(50, 30), s=100)
for ax in axes.flatten():
ax.xaxis.label.set_rotation(90)
ax.yaxis.label.set_rotation(0)
ax.yaxis.label.set_ha('right')
plt.show()
In the next cell we try to explore and analyze the correlation status of features in the dataframe. Have in mind that correlation is meaningless for categorical values, thus we have to make these type of values label encoded. have in mind we will only label encode values that we believe will result in meaningful result.
intercor = df.corr(numeric_only=True)
plt.figure(figsize=(25,25))
sns.heatmap(intercor,annot=True, cmap='rocket_r', fmt='.3f')
plt.tight_layout()
plt.title('Features Correlation Heat Map')
plt.show()
In the cells below we will explore the data by grouping features and calculate the aggregations.
df.groupby('Output_Type')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Output_Type | |||||
| Article | 68264 | 3.302854 | 4568 | 0 | 34.969362 |
| Book | 98 | 1.204082 | 27 | 0 | 3.220294 |
| Chapter | 5646 | 0.246192 | 220 | 0 | 4.284104 |
| News | 1 | 26.000000 | 26 | 26 | NaN |
df.groupby('OA_Status')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| OA_Status | |||||
| False | 48156 | 1.855117 | 790 | 0 | 9.528782 |
| True | 25853 | 5.324914 | 4568 | 0 | 55.299093 |
An interesting insight that we can deduce from the table above, is that publishing under open access policies will result in better performing and more mentions. Although the number of articles under open access policies are lower that closed ones, they resulted in more mentions on average than the closed ones.
df.groupby('OA_Type')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std'])
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| OA_Type | |||||
| bronze | 2656 | 9.338479 | 4568 | 0 | 116.910033 |
| closed | 48155 | 1.855155 | 790 | 0 | 9.528877 |
| gold | 11523 | 4.090167 | 1410 | 0 | 22.571000 |
| green | 9690 | 4.310630 | 2766 | 0 | 47.675711 |
| hybrid | 1985 | 12.071033 | 1502 | 0 | 86.116285 |
df.groupby('Publisher_Names')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Publisher_Names | |||||
| Elsevier BV | 21585 | 2.279129 | 696 | 0 | 11.427640 |
| Springer Nature | 13776 | 1.528818 | 1017 | 0 | 10.665141 |
| Institute of Electrical and Electronics Engineers (IEEE) | 4148 | 1.224446 | 95 | 0 | 3.094993 |
| ESSOAr; Natural History Museum; Wiley | 3991 | 2.306439 | 426 | 0 | 11.489564 |
| GeoScienceWorld; Taylor & Francis | 3633 | 2.218277 | 282 | 0 | 10.420666 |
| ... | ... | ... | ... | ... | ... |
| Oxford University Press (OUP); Taylor & Francis | 1 | 0.000000 | 0 | 0 | NaN |
| American Diabetes Association | 1 | 6.000000 | 6 | 6 | NaN |
| Hindawi Limited; Springer Nature | 1 | 3.000000 | 3 | 3 | NaN |
| Consortium Erudit; GeoScienceWorld; Taylor & Francis | 1 | 3.000000 | 3 | 3 | NaN |
| American Thoracic Society; StatRef | 1 | 1.000000 | 1 | 1 | NaN |
189 rows × 5 columns
df.groupby('University')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| University | |||||
| University of Tehran | 16194 | 3.995863 | 4568 | 0 | 56.224681 |
| Tarbiat Modares University | 8870 | 3.917249 | 2043 | 0 | 35.927520 |
| Sharif University of Technology | 6866 | 2.918730 | 1367 | 0 | 23.913502 |
| Amir Kabir University | 5753 | 1.788632 | 1367 | 0 | 22.824887 |
| University of Shiraz | 5572 | 2.689698 | 633 | 0 | 15.228685 |
| Ferdowsi University of Mashhad | 4910 | 3.114053 | 1017 | 0 | 21.330601 |
| Shahid Beheshti University | 4793 | 3.412477 | 1410 | 0 | 27.233875 |
| Isfahan University of Technology | 4724 | 2.270957 | 372 | 0 | 9.669328 |
| Iran University of Science and Technology | 4356 | 1.435262 | 507 | 0 | 9.761763 |
| University of Tabriz | 4130 | 1.948184 | 349 | 0 | 8.935398 |
| University of Isfahan | 3103 | 3.471157 | 623 | 0 | 19.017597 |
| K. N. Toosi University of Technology | 2103 | 1.725630 | 221 | 0 | 7.409996 |
| University of Guilan | 2049 | 3.160566 | 790 | 0 | 21.222986 |
| Allameh Tabataba'i University | 461 | 9.624729 | 1629 | 0 | 88.069070 |
| Imam Hossein University | 125 | 1.944000 | 25 | 0 | 3.046161 |
It's worth to mention that, Although Allameh Tabataba'i University has the second to last amount of research output, it has the highest amount of Altmetric attention score on average. It's standard deviation is also highest among the universities. This fact indicates an outlier among the publications of this university.
df.groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 16476 | 1.444829 | 661 | 0 | 8.146969 |
| 11 Medical and Health Sciences | 10732 | 6.795471 | 4568 | 0 | 61.368467 |
| 03 Chemical Sciences | 10042 | 1.832503 | 201 | 0 | 4.575673 |
| 06 Biological Sciences | 8161 | 4.476045 | 2766 | 0 | 42.644529 |
| 08 Information and Computing Sciences | 6783 | 1.423411 | 1017 | 0 | 13.187518 |
| 01 Mathematical Sciences | 5183 | 1.368705 | 190 | 0 | 5.173754 |
| 02 Physical Sciences | 4605 | 2.786102 | 329 | 0 | 10.989553 |
| 04 Earth Sciences | 2807 | 4.155682 | 3149 | 0 | 61.895835 |
| 05 Environmental Sciences | 1785 | 4.882913 | 961 | 0 | 33.274107 |
| 07 Agricultural and Veterinary Sciences | 1213 | 1.930750 | 202 | 0 | 6.806992 |
| 17 Psychology and Cognitive Sciences | 1167 | 5.036847 | 333 | 0 | 20.024843 |
| 10 Technology | 1146 | 1.419721 | 140 | 0 | 5.251002 |
| 15 Commerce, Management, Tourism and Services | 699 | 1.589413 | 141 | 0 | 7.767499 |
| 14 Economics | 636 | 2.962264 | 409 | 0 | 19.583904 |
| 16 Studies in Human Society | 590 | 3.288136 | 175 | 0 | 11.463755 |
| 13 Education | 427 | 2.405152 | 66 | 0 | 6.048107 |
| 00 No Category | 383 | 0.950392 | 69 | 0 | 4.294785 |
| 12 Built Environment and Design | 333 | 1.531532 | 36 | 0 | 3.895054 |
| 20 Language, Communication and Culture | 203 | 2.004926 | 31 | 0 | 4.133296 |
| 21 History and Archaeology | 138 | 51.557971 | 2043 | 0 | 281.978050 |
| 22 Philosophy and Religious Studies | 121 | 2.776860 | 60 | 0 | 7.356955 |
| 40 Engineering | 82 | 0.792683 | 10 | 0 | 1.810380 |
| 32 Biomedical and Clinical Sciences | 56 | 1.375000 | 23 | 0 | 3.887334 |
| 18 Law and Legal Studies | 43 | 6.627907 | 56 | 0 | 13.569966 |
| 31 Biological Sciences | 39 | 1.923077 | 16 | 0 | 3.055492 |
| 46 Information and Computing Sciences | 25 | 1.040000 | 11 | 0 | 2.406242 |
| 49 Mathematical Sciences | 20 | 0.150000 | 2 | 0 | 0.489360 |
| 34 Chemical Sciences | 17 | 1.764706 | 8 | 0 | 1.953504 |
| 19 Studies in Creative Arts and Writing | 16 | 2.437500 | 10 | 0 | 3.182635 |
| 37 Earth Sciences | 14 | 0.714286 | 3 | 0 | 1.069045 |
| 35 Commerce, Management, Tourism and Services | 13 | 1.153846 | 3 | 0 | 1.143544 |
| 44 Human Society | 8 | 1.750000 | 3 | 1 | 0.886405 |
| 30 Agricultural, Veterinary and Food Sciences | 8 | 2.000000 | 9 | 0 | 3.023716 |
| 50 Philosophy and Religious Studies | 8 | 17.250000 | 66 | 0 | 30.103393 |
| 33 Built Environment and Design | 6 | 1.166667 | 2 | 0 | 0.752773 |
| 51 Physical Sciences | 6 | 2.166667 | 9 | 0 | 3.488075 |
| 52 Psychology | 5 | 2.200000 | 7 | 1 | 2.683282 |
| 42 Health Sciences | 3 | 1.000000 | 1 | 1 | 0.000000 |
| 41 Environmental Sciences | 3 | 0.666667 | 1 | 0 | 0.577350 |
| 47 Language, Communication and Culture | 2 | 0.000000 | 0 | 0 | 0.000000 |
| 39 Education | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 38 Economics | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 36 Creative Arts and Writing | 1 | 0.000000 | 0 | 0 | NaN |
As it was discussed earlier, the majority of research output had Engineering as their category. But the majority of attentions was retrieved by the research output which had Medical and Health Sciences as their first category. This trait is probably due to the Covid-19 pandemic and the fact that research about this pandemic retrieved much more attention in the media.
Another interesting insight, is the fact that some research outputs in category of 21 History and Archaeology attract some attention, after further investigation, it was concluded that there is a research output with about 2000 attention score that was both credited to University of Tehran and Tarbiat Modares University.
df.groupby('Category_2')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_2 | |||||
| 40 Engineering | 17490 | 1.260835 | 369 | 0 | 4.751832 |
| 32 Biomedical and Clinical Sciences | 7645 | 6.060562 | 4568 | 0 | 60.612184 |
| 34 Chemical Sciences | 7452 | 1.785293 | 182 | 0 | 4.002456 |
| 31 Biological Sciences | 6882 | 4.774920 | 1629 | 0 | 36.021628 |
| 46 Information and Computing Sciences | 4580 | 1.340175 | 1017 | 0 | 15.497656 |
| 09 Engineering | 3873 | 1.709786 | 191 | 0 | 5.868211 |
| 30 Agricultural, Veterinary and Food Sciences | 3487 | 3.680528 | 2043 | 0 | 55.416739 |
| 37 Earth Sciences | 2817 | 4.046858 | 3149 | 0 | 62.551944 |
| 51 Physical Sciences | 2771 | 3.561891 | 507 | 0 | 17.742544 |
| 49 Mathematical Sciences | 2163 | 1.509940 | 45 | 0 | 2.946684 |
| 42 Health Sciences | 1416 | 9.480932 | 1367 | 0 | 70.732720 |
| 35 Commerce, Management, Tourism and Services | 1243 | 1.448914 | 179 | 0 | 8.003598 |
| 11 Medical and Health Sciences | 1061 | 3.835061 | 948 | 0 | 29.741752 |
| 06 Biological Sciences | 939 | 6.604899 | 961 | 0 | 43.754115 |
| 33 Built Environment and Design | 895 | 2.879330 | 661 | 0 | 25.183465 |
| 02 Physical Sciences | 826 | 2.829298 | 102 | 0 | 8.106483 |
| 10 Technology | 771 | 1.688716 | 28 | 0 | 2.876978 |
| 41 Environmental Sciences | 764 | 3.738220 | 897 | 0 | 33.659922 |
| 07 Agricultural and Veterinary Sciences | 738 | 1.897019 | 49 | 0 | 3.412837 |
| 17 Psychology and Cognitive Sciences | 734 | 6.359673 | 585 | 0 | 32.664048 |
| 08 Information and Computing Sciences | 677 | 1.109306 | 37 | 0 | 2.475296 |
| 52 Psychology | 654 | 7.568807 | 471 | 0 | 32.072668 |
| 00 No Category | 582 | 1.130584 | 69 | 0 | 3.879439 |
| 05 Environmental Sciences | 397 | 2.617128 | 215 | 0 | 11.783806 |
| 44 Human Society | 375 | 3.765333 | 111 | 0 | 10.423563 |
| 03 Chemical Sciences | 342 | 3.017544 | 134 | 0 | 10.337685 |
| 38 Economics | 319 | 2.344828 | 119 | 0 | 9.500425 |
| 39 Education | 317 | 2.372240 | 44 | 0 | 5.253713 |
| 12 Built Environment and Design | 292 | 3.071918 | 243 | 0 | 17.173812 |
| 15 Commerce, Management, Tourism and Services | 260 | 1.334615 | 61 | 0 | 5.533293 |
| 16 Studies in Human Society | 178 | 3.949438 | 66 | 0 | 9.710075 |
| 14 Economics | 174 | 1.689655 | 31 | 0 | 3.868684 |
| 47 Language, Communication and Culture | 163 | 2.036810 | 24 | 0 | 3.656365 |
| 04 Earth Sciences | 125 | 2.120000 | 16 | 0 | 2.894934 |
| 50 Philosophy and Religious Studies | 117 | 3.470085 | 60 | 0 | 8.375429 |
| 43 History, Heritage and Archaeology | 104 | 5.644231 | 88 | 0 | 14.084294 |
| 20 Language, Communication and Culture | 103 | 1.611650 | 22 | 0 | 2.762611 |
| 21 History and Archaeology | 60 | 91.933333 | 2766 | 0 | 374.473681 |
| 36 Creative Arts and Writing | 48 | 2.604167 | 31 | 0 | 4.832248 |
| 48 Law and Legal Studies | 41 | 6.731707 | 56 | 0 | 13.881686 |
| 22 Philosophy and Religious Studies | 38 | 6.657895 | 175 | 0 | 28.223314 |
| 13 Education | 37 | 2.000000 | 13 | 0 | 3.291403 |
| 18 Law and Legal Studies | 31 | 1.741935 | 9 | 0 | 2.780500 |
| 19 Studies in Creative Arts and Writing | 28 | 1.642857 | 8 | 0 | 1.591977 |
df.groupby('Category_3')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_3 | |||||
| 00 No Category | 48454 | 2.858773 | 4568 | 0 | 31.506775 |
| 40 Engineering | 5978 | 1.650719 | 201 | 0 | 5.741645 |
| 46 Information and Computing Sciences | 2977 | 1.425596 | 357 | 0 | 8.054330 |
| 51 Physical Sciences | 2184 | 2.132784 | 173 | 0 | 6.222088 |
| 31 Biological Sciences | 1932 | 10.700311 | 2766 | 0 | 106.481177 |
| 32 Biomedical and Clinical Sciences | 1753 | 5.353679 | 1629 | 0 | 48.307701 |
| 34 Chemical Sciences | 1293 | 2.003094 | 134 | 0 | 5.454852 |
| 49 Mathematical Sciences | 1134 | 1.514109 | 59 | 0 | 4.306369 |
| 42 Health Sciences | 976 | 8.141393 | 409 | 0 | 28.374031 |
| 30 Agricultural, Veterinary and Food Sciences | 950 | 1.887368 | 110 | 0 | 4.892625 |
| 41 Environmental Sciences | 947 | 3.059134 | 430 | 0 | 19.006249 |
| 09 Engineering | 678 | 2.246313 | 102 | 0 | 8.314699 |
| 52 Psychology | 673 | 7.595840 | 288 | 0 | 26.560243 |
| 37 Earth Sciences | 653 | 2.540582 | 215 | 0 | 10.938911 |
| 10 Technology | 611 | 2.217676 | 191 | 0 | 8.242836 |
| 44 Human Society | 387 | 14.953488 | 1367 | 0 | 120.638856 |
| 35 Commerce, Management, Tourism and Services | 301 | 1.631229 | 61 | 0 | 6.369737 |
| 33 Built Environment and Design | 278 | 2.672662 | 243 | 0 | 14.945534 |
| 11 Medical and Health Sciences | 275 | 3.047273 | 191 | 0 | 11.871327 |
| 06 Biological Sciences | 175 | 2.411429 | 55 | 0 | 6.605630 |
| 38 Economics | 173 | 5.086705 | 661 | 0 | 50.224363 |
| 47 Language, Communication and Culture | 166 | 2.584337 | 42 | 0 | 4.666008 |
| 15 Commerce, Management, Tourism and Services | 154 | 1.461039 | 109 | 0 | 8.872468 |
| 17 Psychology and Cognitive Sciences | 133 | 1.827068 | 44 | 0 | 4.484842 |
| 07 Agricultural and Veterinary Sciences | 111 | 1.270270 | 17 | 0 | 2.276145 |
| 39 Education | 110 | 1.854545 | 15 | 0 | 2.573101 |
| 16 Studies in Human Society | 104 | 2.528846 | 31 | 0 | 5.307911 |
| 50 Philosophy and Religious Studies | 83 | 3.807229 | 175 | 0 | 19.158809 |
| 08 Information and Computing Sciences | 74 | 2.175676 | 13 | 0 | 2.953327 |
| 48 Law and Legal Studies | 72 | 2.777778 | 52 | 0 | 7.027556 |
| 43 History, Heritage and Archaeology | 55 | 26.854545 | 792 | 0 | 115.114342 |
| 20 Language, Communication and Culture | 34 | 0.970588 | 8 | 0 | 1.660327 |
| 12 Built Environment and Design | 25 | 1.960000 | 12 | 0 | 3.034249 |
| 03 Chemical Sciences | 22 | 0.772727 | 3 | 0 | 0.869144 |
| 36 Creative Arts and Writing | 15 | 1.066667 | 8 | 0 | 2.086236 |
| 22 Philosophy and Religious Studies | 13 | 2.076923 | 9 | 0 | 2.531848 |
| 05 Environmental Sciences | 13 | 0.615385 | 2 | 0 | 0.650444 |
| 18 Law and Legal Studies | 12 | 1.750000 | 4 | 0 | 1.138180 |
| 14 Economics | 10 | 0.900000 | 6 | 0 | 1.852926 |
| 19 Studies in Creative Arts and Writing | 10 | 2.000000 | 6 | 0 | 1.943651 |
| 13 Education | 6 | 5.333333 | 19 | 0 | 7.201852 |
| 21 History and Archaeology | 5 | 1.800000 | 6 | 0 | 2.387467 |
df.groupby('Category_4')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_4 | |||||
| 00 No Category | 66832 | 2.879938 | 4568 | 0 | 30.058128 |
| 40 Engineering | 1883 | 1.771110 | 82 | 0 | 3.946935 |
| 46 Information and Computing Sciences | 797 | 1.908407 | 227 | 0 | 13.306888 |
| 51 Physical Sciences | 785 | 2.719745 | 74 | 0 | 6.716408 |
| 41 Environmental Sciences | 643 | 9.191291 | 961 | 0 | 58.319715 |
| 31 Biological Sciences | 616 | 2.217532 | 110 | 0 | 5.558974 |
| 32 Biomedical and Clinical Sciences | 414 | 2.748792 | 63 | 0 | 5.567954 |
| 34 Chemical Sciences | 316 | 2.984177 | 191 | 0 | 12.253787 |
| 49 Mathematical Sciences | 290 | 2.000000 | 102 | 0 | 10.691005 |
| 52 Psychology | 272 | 9.904412 | 585 | 0 | 48.404870 |
| 30 Agricultural, Veterinary and Food Sciences | 222 | 2.072072 | 54 | 0 | 4.724465 |
| 35 Commerce, Management, Tourism and Services | 179 | 1.636872 | 109 | 0 | 8.332081 |
| 37 Earth Sciences | 164 | 3.756098 | 241 | 0 | 19.546742 |
| 44 Human Society | 154 | 9.928571 | 792 | 0 | 66.844773 |
| 42 Health Sciences | 129 | 4.449612 | 59 | 0 | 8.645773 |
| 47 Language, Communication and Culture | 69 | 3.855072 | 66 | 0 | 11.210913 |
| 33 Built Environment and Design | 59 | 1.101695 | 11 | 0 | 2.179014 |
| 38 Economics | 50 | 3.260000 | 108 | 0 | 15.168186 |
| 48 Law and Legal Studies | 37 | 4.837838 | 108 | 0 | 17.659611 |
| 36 Creative Arts and Writing | 29 | 1.931034 | 8 | 0 | 1.869512 |
| 43 History, Heritage and Archaeology | 24 | 442.541667 | 2766 | 0 | 797.265707 |
| 50 Philosophy and Religious Studies | 21 | 1.571429 | 15 | 0 | 3.264528 |
| 39 Education | 18 | 2.277778 | 12 | 0 | 3.922867 |
| 10 Technology | 3 | 0.666667 | 1 | 0 | 0.577350 |
| 09 Engineering | 2 | 1.000000 | 1 | 1 | 0.000000 |
| 11 Medical and Health Sciences | 1 | 6.000000 | 6 | 6 | NaN |
df.groupby('Category_5')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_5 | |||||
| 00 No Category | 72667 | 3.037761 | 4568 | 0 | 32.307099 |
| 40 Engineering | 331 | 2.700906 | 191 | 0 | 11.599816 |
| 41 Environmental Sciences | 204 | 2.049020 | 54 | 0 | 5.236058 |
| 46 Information and Computing Sciences | 177 | 1.468927 | 23 | 0 | 2.569576 |
| 51 Physical Sciences | 145 | 4.172414 | 102 | 0 | 14.840516 |
| 49 Mathematical Sciences | 95 | 1.684211 | 21 | 0 | 3.498520 |
| 34 Chemical Sciences | 82 | 2.036585 | 24 | 0 | 3.469248 |
| 32 Biomedical and Clinical Sciences | 64 | 1.703125 | 11 | 0 | 2.044580 |
| 52 Psychology | 41 | 4.780488 | 27 | 0 | 5.667946 |
| 44 Human Society | 38 | 75.605263 | 2766 | 0 | 448.337924 |
| 31 Biological Sciences | 36 | 1.055556 | 5 | 0 | 1.119807 |
| 42 Health Sciences | 33 | 4.151515 | 44 | 0 | 8.039439 |
| 38 Economics | 24 | 2.416667 | 11 | 0 | 3.525271 |
| 47 Language, Communication and Culture | 17 | 1.235294 | 8 | 0 | 2.136861 |
| 37 Earth Sciences | 13 | 2.000000 | 6 | 0 | 2.198484 |
| 35 Commerce, Management, Tourism and Services | 12 | 0.333333 | 2 | 0 | 0.651339 |
| 48 Law and Legal Studies | 10 | 1.000000 | 3 | 0 | 1.154701 |
| 50 Philosophy and Religious Studies | 9 | 1.000000 | 5 | 0 | 1.581139 |
| 33 Built Environment and Design | 4 | 1.750000 | 6 | 0 | 2.872281 |
| 43 History, Heritage and Archaeology | 4 | 64.500000 | 241 | 0 | 117.854430 |
| 36 Creative Arts and Writing | 3 | 1.666667 | 5 | 0 | 2.886751 |
df.groupby('Category_6')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_6 | |||||
| 00 No Category | 73746 | 3.070865 | 4568 | 0 | 33.674597 |
| 41 Environmental Sciences | 61 | 2.180328 | 24 | 0 | 3.761685 |
| 51 Physical Sciences | 41 | 2.536585 | 15 | 0 | 3.795376 |
| 44 Human Society | 32 | 2.750000 | 22 | 0 | 4.898979 |
| 40 Engineering | 27 | 1.296296 | 10 | 0 | 2.127021 |
| 46 Information and Computing Sciences | 27 | 1.518519 | 10 | 0 | 2.562606 |
| 49 Mathematical Sciences | 22 | 0.454545 | 2 | 0 | 0.670982 |
| 52 Psychology | 19 | 3.789474 | 44 | 0 | 10.003216 |
| 42 Health Sciences | 12 | 1.416667 | 4 | 1 | 0.900337 |
| 36 Creative Arts and Writing | 9 | 2.555556 | 6 | 0 | 2.297341 |
| 32 Biomedical and Clinical Sciences | 4 | 2.000000 | 3 | 1 | 0.816497 |
| 47 Language, Communication and Culture | 4 | 0.500000 | 1 | 0 | 0.577350 |
| 34 Chemical Sciences | 3 | 0.333333 | 1 | 0 | 0.577350 |
| 38 Economics | 2 | 1.000000 | 1 | 1 | 0.000000 |
df.groupby('Category_7')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_7 | |||||
| 00 No Category | 74006 | 3.067238 | 4568 | 0 | 33.616353 |
| 49 Mathematical Sciences | 3 | 2.000000 | 3 | 0 | 1.732051 |
df[df['University'] == "Allameh Tabataba'i University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 11 Medical and Health Sciences | 116 | 29.724138 | 1629 | 0 | 173.372511 |
| 17 Psychology and Cognitive Sciences | 58 | 3.172414 | 31 | 0 | 5.959330 |
| 08 Information and Computing Sciences | 45 | 1.311111 | 13 | 0 | 2.475537 |
| 01 Mathematical Sciences | 39 | 1.717949 | 40 | 0 | 6.353402 |
| 15 Commerce, Management, Tourism and Services | 39 | 3.435897 | 97 | 0 | 15.449059 |
| 13 Education | 34 | 3.088235 | 66 | 0 | 11.163706 |
| 16 Studies in Human Society | 29 | 7.965517 | 175 | 0 | 32.206347 |
| 09 Engineering | 20 | 0.950000 | 4 | 0 | 1.276302 |
| 14 Economics | 20 | 2.700000 | 16 | 0 | 4.612340 |
| 20 Language, Communication and Culture | 19 | 2.578947 | 24 | 0 | 5.610662 |
| 12 Built Environment and Design | 7 | 1.000000 | 3 | 0 | 1.000000 |
| 03 Chemical Sciences | 6 | 1.000000 | 3 | 0 | 1.264911 |
| 21 History and Archaeology | 5 | 1.200000 | 4 | 0 | 1.643168 |
| 22 Philosophy and Religious Studies | 4 | 1.000000 | 3 | 0 | 1.414214 |
| 06 Biological Sciences | 4 | 2.250000 | 6 | 0 | 2.629956 |
| 18 Law and Legal Studies | 4 | 7.750000 | 18 | 0 | 7.500000 |
| 00 No Category | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 10 Technology | 3 | 4.000000 | 9 | 0 | 4.582576 |
| 19 Studies in Creative Arts and Writing | 1 | 0.000000 | 0 | 0 | NaN |
| 07 Agricultural and Veterinary Sciences | 1 | 3.000000 | 3 | 3 | NaN |
| 05 Environmental Sciences | 1 | 4.000000 | 4 | 4 | NaN |
| 02 Physical Sciences | 1 | 3.000000 | 3 | 3 | NaN |
| 35 Commerce, Management, Tourism and Services | 1 | 1.000000 | 1 | 1 | NaN |
| 46 Information and Computing Sciences | 1 | 1.000000 | 1 | 1 | NaN |
df[df['University'] == "Amir Kabir University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 2208 | 1.057518 | 28 | 0 | 1.986554 |
| 08 Information and Computing Sciences | 816 | 1.182598 | 104 | 0 | 4.861744 |
| 03 Chemical Sciences | 791 | 1.656131 | 106 | 0 | 4.291951 |
| 01 Mathematical Sciences | 583 | 0.910806 | 59 | 0 | 2.841567 |
| 11 Medical and Health Sciences | 390 | 9.964103 | 1367 | 0 | 86.568716 |
| 02 Physical Sciences | 257 | 1.466926 | 12 | 0 | 1.932422 |
| 06 Biological Sciences | 137 | 1.956204 | 34 | 0 | 3.886002 |
| 04 Earth Sciences | 132 | 1.416667 | 26 | 0 | 2.887302 |
| 10 Technology | 117 | 0.923077 | 7 | 0 | 1.457169 |
| 00 No Category | 60 | 0.316667 | 6 | 0 | 1.065510 |
| 15 Commerce, Management, Tourism and Services | 50 | 0.580000 | 5 | 0 | 1.070762 |
| 05 Environmental Sciences | 43 | 1.511628 | 22 | 0 | 3.500830 |
| 17 Psychology and Cognitive Sciences | 41 | 1.292683 | 7 | 0 | 1.569138 |
| 14 Economics | 31 | 1.096774 | 9 | 0 | 2.134711 |
| 12 Built Environment and Design | 19 | 1.157895 | 3 | 0 | 1.067872 |
| 16 Studies in Human Society | 17 | 1.411765 | 15 | 0 | 3.742640 |
| 13 Education | 12 | 1.666667 | 3 | 0 | 1.073087 |
| 22 Philosophy and Religious Studies | 9 | 0.888889 | 3 | 0 | 0.927961 |
| 32 Biomedical and Clinical Sciences | 7 | 1.142857 | 8 | 0 | 3.023716 |
| 40 Engineering | 6 | 0.000000 | 0 | 0 | 0.000000 |
| 07 Agricultural and Veterinary Sciences | 6 | 2.500000 | 9 | 0 | 3.728270 |
| 49 Mathematical Sciences | 4 | 0.500000 | 2 | 0 | 1.000000 |
| 21 History and Archaeology | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 35 Commerce, Management, Tourism and Services | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 46 Information and Computing Sciences | 3 | 3.666667 | 11 | 0 | 6.350853 |
| 20 Language, Communication and Culture | 2 | 1.500000 | 3 | 0 | 2.121320 |
| 18 Law and Legal Studies | 2 | 1.000000 | 1 | 1 | 0.000000 |
| 34 Chemical Sciences | 1 | 8.000000 | 8 | 8 | NaN |
| 37 Earth Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 47 Language, Communication and Culture | 1 | 0.000000 | 0 | 0 | NaN |
| 50 Philosophy and Religious Studies | 1 | 0.000000 | 0 | 0 | NaN |
df[df['University'] == "Shahid Beheshti University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 11 Medical and Health Sciences | 895 | 6.423464 | 764 | 0 | 37.688060 |
| 03 Chemical Sciences | 661 | 1.996974 | 60 | 0 | 3.674646 |
| 09 Engineering | 600 | 1.428333 | 97 | 0 | 4.800688 |
| 02 Physical Sciences | 566 | 2.994700 | 219 | 0 | 11.449233 |
| 06 Biological Sciences | 496 | 6.508065 | 1410 | 0 | 64.434008 |
| 08 Information and Computing Sciences | 379 | 1.897098 | 191 | 0 | 10.525226 |
| 01 Mathematical Sciences | 343 | 1.472303 | 19 | 0 | 2.475309 |
| 17 Psychology and Cognitive Sciences | 169 | 5.532544 | 220 | 0 | 18.026117 |
| 04 Earth Sciences | 165 | 2.375758 | 25 | 0 | 4.008149 |
| 05 Environmental Sciences | 121 | 2.768595 | 35 | 0 | 4.900273 |
| 10 Technology | 76 | 1.184211 | 10 | 0 | 1.902353 |
| 16 Studies in Human Society | 52 | 2.134615 | 16 | 0 | 3.211675 |
| 07 Agricultural and Veterinary Sciences | 44 | 2.250000 | 13 | 0 | 3.170540 |
| 14 Economics | 43 | 0.906977 | 7 | 0 | 1.394102 |
| 00 No Category | 34 | 2.205882 | 69 | 0 | 11.813653 |
| 13 Education | 29 | 1.655172 | 9 | 0 | 2.334154 |
| 15 Commerce, Management, Tourism and Services | 27 | 1.740741 | 14 | 0 | 2.781707 |
| 12 Built Environment and Design | 27 | 1.555556 | 22 | 0 | 4.181768 |
| 20 Language, Communication and Culture | 21 | 1.714286 | 18 | 0 | 3.887710 |
| 40 Engineering | 10 | 0.400000 | 3 | 0 | 0.966092 |
| 22 Philosophy and Religious Studies | 10 | 0.200000 | 1 | 0 | 0.421637 |
| 21 History and Archaeology | 7 | 1.571429 | 3 | 0 | 1.272418 |
| 18 Law and Legal Studies | 5 | 0.800000 | 3 | 0 | 1.303840 |
| 49 Mathematical Sciences | 3 | 0.333333 | 1 | 0 | 0.577350 |
| 31 Biological Sciences | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 32 Biomedical and Clinical Sciences | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 33 Built Environment and Design | 1 | 2.000000 | 2 | 2 | NaN |
| 34 Chemical Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 42 Health Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 19 Studies in Creative Arts and Writing | 1 | 0.000000 | 0 | 0 | NaN |
| 50 Philosophy and Religious Studies | 1 | 1.000000 | 1 | 1 | NaN |
| 51 Physical Sciences | 1 | 2.000000 | 2 | 2 | NaN |
df[df['University'] == "Ferdowsi University of Mashhad"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 913 | 1.197152 | 77 | 0 | 3.189984 |
| 06 Biological Sciences | 705 | 4.012766 | 220 | 0 | 14.603417 |
| 11 Medical and Health Sciences | 657 | 5.474886 | 357 | 0 | 25.807089 |
| 03 Chemical Sciences | 628 | 1.815287 | 131 | 0 | 5.690667 |
| 08 Information and Computing Sciences | 409 | 3.493888 | 1017 | 0 | 50.272547 |
| 01 Mathematical Sciences | 352 | 0.920455 | 10 | 0 | 1.588575 |
| 02 Physical Sciences | 259 | 2.467181 | 63 | 0 | 4.962835 |
| 04 Earth Sciences | 230 | 4.517391 | 241 | 0 | 19.943964 |
| 05 Environmental Sciences | 157 | 11.280255 | 538 | 0 | 52.470240 |
| 07 Agricultural and Veterinary Sciences | 151 | 1.761589 | 24 | 0 | 3.097760 |
| 17 Psychology and Cognitive Sciences | 99 | 4.676768 | 139 | 0 | 14.819890 |
| 10 Technology | 54 | 1.333333 | 9 | 0 | 2.136829 |
| 15 Commerce, Management, Tourism and Services | 52 | 2.134615 | 82 | 0 | 11.341454 |
| 13 Education | 51 | 1.333333 | 10 | 0 | 1.544884 |
| 14 Economics | 44 | 0.659091 | 5 | 0 | 0.963115 |
| 16 Studies in Human Society | 35 | 2.514286 | 37 | 0 | 6.227885 |
| 20 Language, Communication and Culture | 27 | 2.111111 | 16 | 0 | 3.377907 |
| 12 Built Environment and Design | 20 | 0.900000 | 3 | 0 | 0.967906 |
| 00 No Category | 14 | 4.285714 | 25 | 0 | 8.165863 |
| 22 Philosophy and Religious Studies | 12 | 6.750000 | 46 | 0 | 12.892034 |
| 21 History and Archaeology | 9 | 1.888889 | 5 | 0 | 1.763834 |
| 32 Biomedical and Clinical Sciences | 5 | 9.400000 | 23 | 0 | 9.555103 |
| 40 Engineering | 5 | 0.000000 | 0 | 0 | 0.000000 |
| 19 Studies in Creative Arts and Writing | 4 | 3.500000 | 8 | 0 | 3.696846 |
| 31 Biological Sciences | 4 | 4.250000 | 11 | 0 | 4.716991 |
| 34 Chemical Sciences | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 35 Commerce, Management, Tourism and Services | 2 | 3.000000 | 3 | 3 | 0.000000 |
| 50 Philosophy and Religious Studies | 2 | 1.000000 | 1 | 1 | 0.000000 |
| 37 Earth Sciences | 1 | 3.000000 | 3 | 3 | NaN |
| 38 Economics | 1 | 1.000000 | 1 | 1 | NaN |
| 39 Education | 1 | 3.000000 | 3 | 3 | NaN |
| 30 Agricultural, Veterinary and Food Sciences | 1 | 3.000000 | 3 | 3 | NaN |
| 44 Human Society | 1 | 1.000000 | 1 | 1 | NaN |
| 46 Information and Computing Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 49 Mathematical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 52 Psychology | 1 | 1.000000 | 1 | 1 | NaN |
df[df['University'] == "University of Guilan"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 370 | 1.621622 | 100 | 0 | 5.938033 |
| 06 Biological Sciences | 361 | 4.277008 | 295 | 0 | 19.403772 |
| 11 Medical and Health Sciences | 288 | 9.291667 | 790 | 0 | 50.066976 |
| 03 Chemical Sciences | 282 | 1.652482 | 14 | 0 | 1.842503 |
| 02 Physical Sciences | 231 | 1.333333 | 31 | 0 | 3.212295 |
| 01 Mathematical Sciences | 109 | 1.055046 | 13 | 0 | 1.819823 |
| 08 Information and Computing Sciences | 102 | 1.166667 | 22 | 0 | 2.552744 |
| 05 Environmental Sciences | 74 | 3.324324 | 182 | 0 | 21.081127 |
| 07 Agricultural and Veterinary Sciences | 62 | 1.483871 | 10 | 0 | 2.078318 |
| 04 Earth Sciences | 40 | 1.150000 | 12 | 0 | 2.019774 |
| 17 Psychology and Cognitive Sciences | 25 | 3.200000 | 24 | 1 | 5.730038 |
| 16 Studies in Human Society | 21 | 1.666667 | 9 | 0 | 1.906130 |
| 10 Technology | 18 | 1.055556 | 3 | 0 | 0.937595 |
| 13 Education | 15 | 3.600000 | 34 | 0 | 8.517209 |
| 15 Commerce, Management, Tourism and Services | 11 | 0.909091 | 3 | 0 | 0.943880 |
| 00 No Category | 10 | 2.400000 | 17 | 0 | 5.253570 |
| 12 Built Environment and Design | 7 | 1.000000 | 1 | 1 | 0.000000 |
| 14 Economics | 7 | 1.000000 | 3 | 0 | 1.000000 |
| 20 Language, Communication and Culture | 4 | 3.500000 | 4 | 2 | 1.000000 |
| 22 Philosophy and Religious Studies | 3 | 1.333333 | 3 | 0 | 1.527525 |
| 31 Biological Sciences | 3 | 1.333333 | 2 | 1 | 0.577350 |
| 18 Law and Legal Studies | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 34 Chemical Sciences | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 21 History and Archaeology | 1 | 0.000000 | 0 | 0 | NaN |
| 32 Biomedical and Clinical Sciences | 1 | 1.000000 | 1 | 1 | NaN |
df[df['University'] == "Imam Hossein University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 03 Chemical Sciences | 44 | 2.090909 | 10 | 0 | 2.009491 |
| 11 Medical and Health Sciences | 26 | 2.846154 | 25 | 0 | 5.334359 |
| 09 Engineering | 14 | 1.000000 | 6 | 0 | 1.797434 |
| 02 Physical Sciences | 11 | 1.272727 | 6 | 0 | 1.618080 |
| 01 Mathematical Sciences | 7 | 0.714286 | 2 | 0 | 0.755929 |
| 08 Information and Computing Sciences | 7 | 1.285714 | 3 | 0 | 1.253566 |
| 17 Psychology and Cognitive Sciences | 6 | 3.166667 | 11 | 0 | 4.490731 |
| 06 Biological Sciences | 4 | 2.250000 | 4 | 1 | 1.500000 |
| 00 No Category | 1 | 3.000000 | 3 | 3 | NaN |
| 04 Earth Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 07 Agricultural and Veterinary Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 12 Built Environment and Design | 1 | 1.000000 | 1 | 1 | NaN |
| 14 Economics | 1 | 0.000000 | 0 | 0 | NaN |
| 34 Chemical Sciences | 1 | 3.000000 | 3 | 3 | NaN |
df[df['University'] == "University of Isfahan"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 11 Medical and Health Sciences | 793 | 7.934426 | 623 | 0 | 35.987242 |
| 03 Chemical Sciences | 487 | 1.874743 | 55 | 0 | 3.833832 |
| 09 Engineering | 395 | 1.579747 | 74 | 0 | 4.661713 |
| 06 Biological Sciences | 349 | 2.289398 | 66 | 0 | 4.608766 |
| 08 Information and Computing Sciences | 241 | 1.560166 | 47 | 0 | 3.763519 |
| 01 Mathematical Sciences | 173 | 1.023121 | 19 | 0 | 2.048691 |
| 02 Physical Sciences | 166 | 1.072289 | 12 | 0 | 1.305310 |
| 17 Psychology and Cognitive Sciences | 109 | 4.908257 | 117 | 0 | 15.432974 |
| 04 Earth Sciences | 81 | 2.469136 | 23 | 0 | 4.505792 |
| 15 Commerce, Management, Tourism and Services | 60 | 1.133333 | 8 | 0 | 1.770442 |
| 13 Education | 51 | 2.313725 | 17 | 0 | 3.770890 |
| 10 Technology | 34 | 1.000000 | 6 | 0 | 1.348400 |
| 05 Environmental Sciences | 32 | 5.812500 | 114 | 0 | 20.233217 |
| 16 Studies in Human Society | 29 | 4.241379 | 64 | 0 | 11.897344 |
| 20 Language, Communication and Culture | 23 | 1.826087 | 9 | 0 | 2.405527 |
| 21 History and Archaeology | 18 | 1.555556 | 4 | 0 | 1.041618 |
| 12 Built Environment and Design | 14 | 0.928571 | 5 | 0 | 1.491735 |
| 14 Economics | 14 | 1.357143 | 5 | 0 | 1.736803 |
| 00 No Category | 9 | 1.333333 | 10 | 0 | 3.278719 |
| 07 Agricultural and Veterinary Sciences | 8 | 0.625000 | 2 | 0 | 0.744024 |
| 22 Philosophy and Religious Studies | 6 | 1.166667 | 3 | 0 | 0.983192 |
| 18 Law and Legal Studies | 3 | 4.000000 | 9 | 0 | 4.582576 |
| 31 Biological Sciences | 2 | 1.500000 | 3 | 0 | 2.121320 |
| 44 Human Society | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 30 Agricultural, Veterinary and Food Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 32 Biomedical and Clinical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 46 Information and Computing Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 51 Physical Sciences | 1 | 2.000000 | 2 | 2 | NaN |
df[df['University'] == "Isfahan University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 1287 | 1.590521 | 135 | 0 | 5.982557 |
| 03 Chemical Sciences | 793 | 1.559899 | 39 | 0 | 2.388262 |
| 02 Physical Sciences | 567 | 4.128748 | 238 | 0 | 12.154744 |
| 01 Mathematical Sciences | 449 | 2.821826 | 190 | 0 | 11.233296 |
| 06 Biological Sciences | 415 | 2.392771 | 86 | 0 | 6.073817 |
| 08 Information and Computing Sciences | 412 | 0.720874 | 10 | 0 | 1.435474 |
| 05 Environmental Sciences | 215 | 2.827907 | 66 | 0 | 8.515141 |
| 11 Medical and Health Sciences | 148 | 4.337838 | 98 | 0 | 12.035409 |
| 04 Earth Sciences | 144 | 6.236111 | 372 | 0 | 36.190635 |
| 07 Agricultural and Veterinary Sciences | 117 | 1.649573 | 11 | 0 | 2.229567 |
| 10 Technology | 60 | 1.166667 | 14 | 0 | 2.293444 |
| 00 No Category | 53 | 0.264151 | 6 | 0 | 1.162738 |
| 12 Built Environment and Design | 12 | 0.833333 | 1 | 0 | 0.389249 |
| 14 Economics | 11 | 0.636364 | 3 | 0 | 1.026911 |
| 16 Studies in Human Society | 9 | 0.888889 | 3 | 0 | 1.054093 |
| 17 Psychology and Cognitive Sciences | 7 | 4.285714 | 21 | 0 | 7.653197 |
| 13 Education | 5 | 0.600000 | 2 | 0 | 0.894427 |
| 15 Commerce, Management, Tourism and Services | 5 | 1.200000 | 3 | 0 | 1.095445 |
| 32 Biomedical and Clinical Sciences | 4 | 0.000000 | 0 | 0 | 0.000000 |
| 40 Engineering | 4 | 0.250000 | 1 | 0 | 0.500000 |
| 18 Law and Legal Studies | 3 | 16.000000 | 44 | 0 | 24.331050 |
| 30 Agricultural, Veterinary and Food Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 31 Biological Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 33 Built Environment and Design | 1 | 1.000000 | 1 | 1 | NaN |
| 52 Psychology | 1 | 7.000000 | 7 | 7 | NaN |
df[df['University'] == "K. N. Toosi University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 752 | 1.285904 | 63 | 0 | 3.891253 |
| 08 Information and Computing Sciences | 325 | 1.363077 | 57 | 0 | 3.650282 |
| 03 Chemical Sciences | 284 | 1.679577 | 17 | 0 | 2.172622 |
| 01 Mathematical Sciences | 188 | 0.851064 | 11 | 0 | 1.447531 |
| 11 Medical and Health Sciences | 123 | 3.943089 | 221 | 0 | 19.980031 |
| 02 Physical Sciences | 120 | 1.816667 | 14 | 0 | 2.425299 |
| 04 Earth Sciences | 112 | 1.732143 | 12 | 0 | 2.471394 |
| 10 Technology | 58 | 1.879310 | 10 | 0 | 2.421233 |
| 06 Biological Sciences | 36 | 2.055556 | 11 | 0 | 3.134549 |
| 05 Environmental Sciences | 30 | 2.733333 | 17 | 0 | 4.101584 |
| 14 Economics | 15 | 23.000000 | 179 | 0 | 52.607713 |
| 17 Psychology and Cognitive Sciences | 10 | 1.200000 | 3 | 0 | 1.316561 |
| 12 Built Environment and Design | 9 | 0.888889 | 2 | 0 | 0.781736 |
| 15 Commerce, Management, Tourism and Services | 9 | 2.555556 | 12 | 0 | 3.711843 |
| 16 Studies in Human Society | 8 | 0.625000 | 2 | 0 | 0.744024 |
| 00 No Category | 5 | 0.200000 | 1 | 0 | 0.447214 |
| 07 Agricultural and Veterinary Sciences | 5 | 1.000000 | 3 | 0 | 1.224745 |
| 18 Law and Legal Studies | 3 | 1.333333 | 4 | 0 | 2.309401 |
| 40 Engineering | 3 | 4.000000 | 6 | 0 | 3.464102 |
| 34 Chemical Sciences | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 19 Studies in Creative Arts and Writing | 1 | 1.000000 | 1 | 1 | NaN |
| 20 Language, Communication and Culture | 1 | 0.000000 | 0 | 0 | NaN |
| 21 History and Archaeology | 1 | 1.000000 | 1 | 1 | NaN |
| 22 Philosophy and Religious Studies | 1 | 0.000000 | 0 | 0 | NaN |
| 31 Biological Sciences | 1 | 2.000000 | 2 | 2 | NaN |
| 32 Biomedical and Clinical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
df[df['University'] == "Sharif University of Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 2001 | 1.469765 | 140 | 0 | 5.305866 |
| 03 Chemical Sciences | 1029 | 2.423712 | 201 | 0 | 8.331604 |
| 08 Information and Computing Sciences | 1014 | 1.288955 | 108 | 0 | 4.367647 |
| 01 Mathematical Sciences | 799 | 1.630788 | 102 | 0 | 6.030424 |
| 02 Physical Sciences | 765 | 4.913725 | 329 | 0 | 21.052363 |
| 11 Medical and Health Sciences | 431 | 13.236659 | 1367 | 0 | 85.964972 |
| 10 Technology | 194 | 1.788660 | 140 | 0 | 10.129814 |
| 06 Biological Sciences | 161 | 5.875776 | 304 | 0 | 24.832126 |
| 04 Earth Sciences | 97 | 4.979381 | 126 | 0 | 17.562970 |
| 14 Economics | 65 | 1.600000 | 14 | 0 | 2.793967 |
| 17 Psychology and Cognitive Sciences | 59 | 1.338983 | 19 | 0 | 3.014964 |
| 15 Commerce, Management, Tourism and Services | 42 | 4.642857 | 141 | 0 | 21.725361 |
| 05 Environmental Sciences | 36 | 1.694444 | 14 | 0 | 2.925938 |
| 00 No Category | 30 | 0.900000 | 15 | 0 | 2.795933 |
| 40 Engineering | 22 | 0.772727 | 6 | 0 | 1.823963 |
| 16 Studies in Human Society | 20 | 2.100000 | 10 | 0 | 2.900091 |
| 13 Education | 17 | 1.823529 | 7 | 0 | 2.038237 |
| 12 Built Environment and Design | 15 | 1.466667 | 6 | 0 | 1.684665 |
| 22 Philosophy and Religious Studies | 14 | 7.214286 | 60 | 0 | 15.870527 |
| 20 Language, Communication and Culture | 13 | 1.769231 | 6 | 0 | 2.087816 |
| 49 Mathematical Sciences | 8 | 0.000000 | 0 | 0 | 0.000000 |
| 34 Chemical Sciences | 6 | 1.666667 | 3 | 0 | 1.211060 |
| 18 Law and Legal Studies | 6 | 1.166667 | 3 | 0 | 1.471960 |
| 07 Agricultural and Veterinary Sciences | 4 | 3.500000 | 10 | 0 | 4.509250 |
| 46 Information and Computing Sciences | 3 | 0.333333 | 1 | 0 | 0.577350 |
| 32 Biomedical and Clinical Sciences | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 21 History and Archaeology | 3 | 3.333333 | 9 | 0 | 4.932883 |
| 19 Studies in Creative Arts and Writing | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 31 Biological Sciences | 2 | 0.000000 | 0 | 0 | 0.000000 |
| 35 Commerce, Management, Tourism and Services | 1 | 1.000000 | 1 | 1 | NaN |
| 36 Creative Arts and Writing | 1 | 0.000000 | 0 | 0 | NaN |
| 37 Earth Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 51 Physical Sciences | 1 | 9.000000 | 9 | 9 | NaN |
df[df['University'] == "Iran University of Science and Technology"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 1696 | 1.650943 | 507 | 0 | 15.099734 |
| 08 Information and Computing Sciences | 677 | 0.927622 | 26 | 0 | 2.034268 |
| 03 Chemical Sciences | 608 | 1.697368 | 48 | 0 | 3.045594 |
| 01 Mathematical Sciences | 484 | 0.756198 | 21 | 0 | 1.562521 |
| 11 Medical and Health Sciences | 175 | 2.440000 | 59 | 0 | 5.929238 |
| 02 Physical Sciences | 170 | 1.088235 | 13 | 0 | 1.583339 |
| 10 Technology | 120 | 0.783333 | 6 | 0 | 1.189473 |
| 04 Earth Sciences | 76 | 2.407895 | 53 | 0 | 7.941751 |
| 06 Biological Sciences | 65 | 2.476923 | 34 | 0 | 5.229447 |
| 05 Environmental Sciences | 53 | 1.396226 | 15 | 0 | 2.256100 |
| 12 Built Environment and Design | 43 | 1.279070 | 14 | 0 | 2.693251 |
| 15 Commerce, Management, Tourism and Services | 43 | 0.604651 | 3 | 0 | 0.954676 |
| 14 Economics | 41 | 0.853659 | 12 | 0 | 2.080396 |
| 17 Psychology and Cognitive Sciences | 40 | 3.200000 | 59 | 0 | 9.855885 |
| 13 Education | 19 | 1.263158 | 3 | 0 | 0.805682 |
| 16 Studies in Human Society | 10 | 1.600000 | 3 | 0 | 1.173788 |
| 00 No Category | 9 | 0.333333 | 3 | 0 | 1.000000 |
| 20 Language, Communication and Culture | 8 | 0.625000 | 3 | 0 | 1.060660 |
| 07 Agricultural and Veterinary Sciences | 4 | 0.000000 | 0 | 0 | 0.000000 |
| 32 Biomedical and Clinical Sciences | 3 | 0.000000 | 0 | 0 | 0.000000 |
| 46 Information and Computing Sciences | 3 | 0.666667 | 1 | 0 | 0.577350 |
| 22 Philosophy and Religious Studies | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 35 Commerce, Management, Tourism and Services | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 21 History and Archaeology | 1 | 3.000000 | 3 | 3 | NaN |
| 34 Chemical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 40 Engineering | 1 | 0.000000 | 0 | 0 | NaN |
| 47 Language, Communication and Culture | 1 | 0.000000 | 0 | 0 | NaN |
| 49 Mathematical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
df[df['University'] == "University of Tabriz"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 856 | 1.386682 | 78 | 0 | 3.819771 |
| 03 Chemical Sciences | 676 | 1.390533 | 16 | 0 | 1.822046 |
| 11 Medical and Health Sciences | 597 | 4.294807 | 349 | 0 | 20.396165 |
| 06 Biological Sciences | 524 | 1.979008 | 33 | 0 | 3.172772 |
| 08 Information and Computing Sciences | 286 | 1.146853 | 11 | 0 | 1.732894 |
| 01 Mathematical Sciences | 266 | 0.725564 | 8 | 0 | 1.103966 |
| 02 Physical Sciences | 265 | 1.769811 | 57 | 0 | 4.081072 |
| 04 Earth Sciences | 205 | 1.946341 | 57 | 0 | 5.777072 |
| 07 Agricultural and Veterinary Sciences | 115 | 3.234783 | 202 | 0 | 18.886703 |
| 05 Environmental Sciences | 111 | 1.378378 | 19 | 0 | 2.475531 |
| 17 Psychology and Cognitive Sciences | 51 | 2.764706 | 20 | 0 | 3.957718 |
| 14 Economics | 44 | 0.295455 | 3 | 0 | 0.667503 |
| 10 Technology | 38 | 2.763158 | 31 | 0 | 6.292141 |
| 12 Built Environment and Design | 22 | 1.681818 | 8 | 0 | 2.056033 |
| 15 Commerce, Management, Tourism and Services | 16 | 1.062500 | 4 | 0 | 1.340087 |
| 16 Studies in Human Society | 11 | 0.636364 | 3 | 0 | 1.026911 |
| 13 Education | 10 | 4.600000 | 28 | 1 | 8.369256 |
| 40 Engineering | 9 | 0.444444 | 3 | 0 | 1.013794 |
| 00 No Category | 8 | 0.500000 | 3 | 0 | 1.069045 |
| 20 Language, Communication and Culture | 7 | 2.142857 | 6 | 0 | 2.410295 |
| 22 Philosophy and Religious Studies | 4 | 2.250000 | 6 | 0 | 2.872281 |
| 31 Biological Sciences | 2 | 1.500000 | 2 | 1 | 0.707107 |
| 37 Earth Sciences | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 18 Law and Legal Studies | 2 | 0.500000 | 1 | 0 | 0.707107 |
| 49 Mathematical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 51 Physical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 52 Psychology | 1 | 1.000000 | 1 | 1 | NaN |
df[df['University'] == "Tarbiat Modares University"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 11 Medical and Health Sciences | 2728 | 5.725073 | 1367 | 0 | 39.696113 |
| 06 Biological Sciences | 1634 | 3.654835 | 554 | 0 | 21.676706 |
| 03 Chemical Sciences | 1219 | 1.759639 | 191 | 0 | 5.853618 |
| 09 Engineering | 1166 | 1.572899 | 182 | 0 | 6.109256 |
| 08 Information and Computing Sciences | 398 | 1.153266 | 13 | 0 | 1.814891 |
| 04 Earth Sciences | 331 | 2.540785 | 224 | 0 | 17.122217 |
| 01 Mathematical Sciences | 246 | 1.150407 | 14 | 0 | 1.924530 |
| 05 Environmental Sciences | 239 | 11.364017 | 961 | 0 | 74.816002 |
| 02 Physical Sciences | 194 | 1.376289 | 13 | 0 | 2.009731 |
| 10 Technology | 136 | 1.794118 | 50 | 0 | 4.960761 |
| 07 Agricultural and Veterinary Sciences | 135 | 1.600000 | 11 | 0 | 2.130798 |
| 17 Psychology and Cognitive Sciences | 86 | 3.430233 | 65 | 0 | 9.368961 |
| 14 Economics | 62 | 3.048387 | 58 | 0 | 8.136998 |
| 16 Studies in Human Society | 62 | 4.532258 | 136 | 0 | 17.614932 |
| 15 Commerce, Management, Tourism and Services | 59 | 1.779661 | 60 | 0 | 7.900388 |
| 13 Education | 56 | 2.000000 | 21 | 0 | 3.668044 |
| 12 Built Environment and Design | 32 | 1.968750 | 36 | 0 | 6.286105 |
| 22 Philosophy and Religious Studies | 17 | 4.117647 | 19 | 0 | 5.765006 |
| 00 No Category | 15 | 1.066667 | 9 | 0 | 2.685056 |
| 20 Language, Communication and Culture | 13 | 3.461538 | 31 | 0 | 8.362769 |
| 21 History and Archaeology | 12 | 241.166667 | 2043 | 0 | 610.841719 |
| 32 Biomedical and Clinical Sciences | 7 | 1.000000 | 5 | 0 | 1.825742 |
| 40 Engineering | 6 | 1.333333 | 3 | 0 | 1.366260 |
| 31 Biological Sciences | 3 | 1.000000 | 2 | 0 | 1.000000 |
| 46 Information and Computing Sciences | 3 | 2.333333 | 6 | 0 | 3.214550 |
| 19 Studies in Creative Arts and Writing | 2 | 2.000000 | 3 | 1 | 1.414214 |
| 18 Law and Legal Studies | 2 | 22.000000 | 43 | 1 | 29.698485 |
| 34 Chemical Sciences | 1 | 2.000000 | 2 | 2 | NaN |
| 35 Commerce, Management, Tourism and Services | 1 | 0.000000 | 0 | 0 | NaN |
| 37 Earth Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 41 Environmental Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 42 Health Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 44 Human Society | 1 | 2.000000 | 2 | 2 | NaN |
| 52 Psychology | 1 | 1.000000 | 1 | 1 | NaN |
df[df['University'] == "University of Tehran"].groupby('Category_1')['Altmetric_Attention_Score'].agg(['count', 'mean', 'max', 'min', 'std']).sort_values(by='count', ascending=False)
| count | mean | max | min | std | |
|---|---|---|---|---|---|
| Category_1 | |||||
| 09 Engineering | 3134 | 1.620294 | 661 | 0 | 12.300163 |
| 11 Medical and Health Sciences | 2564 | 7.201248 | 4568 | 0 | 93.731110 |
| 06 Biological Sciences | 2469 | 6.144998 | 2766 | 0 | 66.144855 |
| 03 Chemical Sciences | 1736 | 1.908410 | 60 | 0 | 3.682534 |
| 08 Information and Computing Sciences | 1320 | 1.409848 | 177 | 0 | 5.738639 |
| 04 Earth Sciences | 919 | 6.078346 | 3149 | 0 | 104.804675 |
| 01 Mathematical Sciences | 847 | 1.573790 | 109 | 0 | 5.616080 |
| 02 Physical Sciences | 614 | 2.464169 | 92 | 0 | 6.164508 |
| 05 Environmental Sciences | 474 | 4.324895 | 218 | 0 | 16.078689 |
| 07 Agricultural and Veterinary Sciences | 375 | 1.640000 | 63 | 0 | 3.780190 |
| 17 Psychology and Cognitive Sciences | 314 | 8.219745 | 333 | 0 | 32.669701 |
| 15 Commerce, Management, Tourism and Services | 268 | 1.119403 | 24 | 0 | 2.681731 |
| 16 Studies in Human Society | 236 | 2.830508 | 44 | 0 | 5.868495 |
| 10 Technology | 199 | 1.417085 | 67 | 0 | 5.005241 |
| 14 Economics | 191 | 4.916230 | 409 | 0 | 31.699133 |
| 00 No Category | 98 | 0.602041 | 7 | 0 | 1.419596 |
| 13 Education | 90 | 3.444444 | 66 | 0 | 8.968247 |
| 12 Built Environment and Design | 81 | 1.382716 | 22 | 0 | 2.790913 |
| 21 History and Archaeology | 69 | 58.202899 | 2043 | 0 | 302.322120 |
| 20 Language, Communication and Culture | 56 | 1.928571 | 31 | 0 | 4.548155 |
| 22 Philosophy and Religious Studies | 34 | 1.352941 | 4 | 0 | 1.432995 |
| 32 Biomedical and Clinical Sciences | 20 | 0.500000 | 3 | 0 | 1.100239 |
| 31 Biological Sciences | 16 | 1.562500 | 5 | 0 | 1.504161 |
| 40 Engineering | 15 | 1.266667 | 10 | 0 | 2.631313 |
| 18 Law and Legal Studies | 10 | 12.300000 | 56 | 0 | 20.265186 |
| 46 Information and Computing Sciences | 9 | 0.333333 | 1 | 0 | 0.500000 |
| 37 Earth Sciences | 7 | 0.571429 | 3 | 0 | 1.133893 |
| 19 Studies in Creative Arts and Writing | 4 | 5.000000 | 10 | 1 | 3.915780 |
| 30 Agricultural, Veterinary and Food Sciences | 4 | 3.000000 | 9 | 0 | 4.082483 |
| 44 Human Society | 4 | 1.750000 | 3 | 1 | 0.957427 |
| 33 Built Environment and Design | 3 | 1.333333 | 2 | 1 | 0.577350 |
| 35 Commerce, Management, Tourism and Services | 3 | 1.000000 | 1 | 1 | 0.000000 |
| 50 Philosophy and Religious Studies | 3 | 23.000000 | 66 | 0 | 37.269290 |
| 51 Physical Sciences | 2 | 0.000000 | 0 | 0 | 0.000000 |
| 49 Mathematical Sciences | 1 | 0.000000 | 0 | 0 | NaN |
| 39 Education | 1 | 1.000000 | 1 | 1 | NaN |
| 42 Health Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 41 Environmental Sciences | 1 | 1.000000 | 1 | 1 | NaN |
| 38 Economics | 1 | 0.000000 | 0 | 0 | NaN |
| 52 Psychology | 1 | 1.000000 | 1 | 1 | NaN |